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11 mai 2021
- doi: 10.4000/books.aaccademia.6819
- eisbn: 9791280136329
https://creativecommons.org/licenses/by-nc-nd/4.0/ , info:eu-repo/semantics/openAccess
Mots-clés
EVALITA COVID-19 Infodemic linguistica computazionale Automatic Misogyny Identification Misogyny on Twitter Posts AlBERTo Convolutional Neural Network BERT Model Hate Speech Detection Multimodal Meme Detection MEME management Language Game ``La Ghigliottina'' Language & Linguistics LAN000000 CBXSujets proches
Women-hatingCiter ce document
El Abassi Samer et al., « MDD @ AMI: Vanilla Classifiers for Misogyny Identification », Accademia University Press, ID : 10.4000/books.aaccademia.6819
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Résumé
In this report, we present a set of vanilla classifiers that we used to identify misogynous and aggressive texts in Italian social media. Our analysis shows that simple classifiers with little feature engineering have a strong tendency to overfit and yield a strong bias on the test set. Additionally, we investigate the usefulness of function words, pronouns, and shallow-syntactical features to observe whether misogynous or aggressive texts have specific stylistic elements.